In this section, we design a deep learning framework for massive device detection in 6G wireless networks. The designed framework aims to address two critical issues in conventional massive device detection approaches. Firstly, the conventional device detection approaches are only applicable to the scenarios of Rayleigh fading channels [1]}, which limits their applicabilities significantly. Secondly, the conventional device detection approaches have high computational complexity.
As shown in Fig. REF , the proposed deep learning framework for massive device detection comprises four modules, i.e., a dimension reduction module, a deep learning network module, an active device detection module, and a channel estimation module. In what follows, we introduce these four modules.
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